#install.packages("readr")
#library(readr)
library(ggplot2)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
#install.packages("GGally")
url <- "https://github.gatech.edu/raw/MGT-6203-Fall-2023-Canvas/Team-18/main/Data/CarSpecs.csv?token=GHSAT0AAAAAAAAC26TN5RFNYUES5XH5SGNMZKFK6TQ"
cardataspec<-read.csv(url)
cardata <- cardataspec
head(cardata)
## Car.Year Car.Make Car.Model MSRP X2019MSRP EPA.Class
## 1 2019 Acura RDX 40600 40600 Small Sport Utility Vehicle
## 2 2019 Acura RDX 45500 45500 Small Sport Utility Vehicle
## 3 2019 Acura RDX 43600 43600 Small Sport Utility Vehicle
## 4 2019 Acura RDX 37400 37400 Small Sport Utility Vehicle
## 5 2019 Acura RDX 42600 42600 Small Sport Utility Vehicle
## 6 2019 Acura RDX 47500 47500 Small Sport Utility Vehicle
## Style.Name Drive.Train Passenger.Capacity Passenger.Doors
## 1 FWD w/Technology Pkg Front Wheel Drive 5 4
## 2 FWD w/Advance Pkg Front Wheel Drive 5 4
## 3 FWD w/A-Spec Pkg Front Wheel Drive 5 4
## 4 FWD Front Wheel Drive 5 4
## 5 AWD w/Technology Pkg All Wheel Drive 5 4
## 6 AWD w/Advance Pkg All Wheel Drive 5 4
## Body.Style Transmittion.Speed Base.Curb.Weight Wheelbase Height.Overall
## 1 Sport Utility 10 3790 108.3 65.7
## 2 Sport Utility 10 3829 108.3 65.7
## 3 Sport Utility 10 3821 108.3 65.7
## 4 Sport Utility 10 3783 108.3 65.7
## 5 Sport Utility 10 4026 108.3 65.7
## 6 Sport Utility 10 4068 108.3 65.7
## Fuel.Tank.Capacity Combined.Estimate.MPG City.MPG Hwy.MPG SAE.Net.Torque
## 1 17.1 24 22 28 280
## 2 17.1 24 22 28 280
## 3 17.1 24 22 27 280
## 4 17.1 24 22 28 280
## 5 17.1 23 21 27 280
## 6 17.1 23 21 27 280
## Fuel.System Engine.Type SAE.Net.Horsepower Transmittion.Description
## 1 Gasoline Injection I4 272 Automatic
## 2 Gasoline Injection I4 272 Automatic
## 3 Gasoline Injection I4 272 Automatic
## 4 Gasoline Injection I4 272 Automatic
## 5 Gasoline Injection I4 272 Automatic
## 6 Gasoline Injection I4 272 Automatic
## Brake.Type Steering.Type Front.Tire.Size Rear.Tire.Size
## 1 4-Wheel Disc Power Rack-Pinion P235/55HR19 P235/55HR19
## 2 4-Wheel Disc Power Rack-Pinion P235/55HR19 P235/55HR19
## 3 4-Wheel Disc Power Rack-Pinion P255/45VR20 P255/45VR20
## 4 4-Wheel Disc Power Rack-Pinion P235/55HR19 P235/55HR19
## 5 4-Wheel Disc Power Rack-Pinion P235/55HR19 P235/55HR19
## 6 4-Wheel Disc Power Rack-Pinion P235/55HR19 P235/55HR19
## Front.Tire.Material Back.Tire.Material Suspension.Type.Front
## 1 Aluminum Aluminum Strut
## 2 Aluminum Aluminum Strut
## 3 Aluminum Aluminum Strut
## 4 Aluminum Aluminum Strut
## 5 Aluminum Aluminum Strut
## 6 Aluminum Aluminum Strut
## Suspension.Type.Rear Brakes.ABS Child.Safety.Rear.Door.Locks
## 1 Multi-Link Yes Yes
## 2 Multi-Link Yes Yes
## 3 Multi-Link Yes Yes
## 4 Multi-Link Yes Yes
## 5 Multi-Link Yes Yes
## 6 Multi-Link Yes Yes
## Daytime.Running.Lights Traction.Control Night.Vision Rollover.Protection.Bars
## 1 Yes Yes No No
## 2 Yes Yes No No
## 3 Yes Yes No No
## 4 Yes Yes No No
## 5 Yes Yes No No
## 6 Yes Yes No No
## Fog.Lamps Parking.Aid Tire.Pressure.Monitor BackUp.Camera Stability.Control
## 1 No Yes Yes Yes Yes
## 2 Yes Yes Yes Yes Yes
## 3 Yes Yes Yes Yes Yes
## 4 No No Yes Yes Yes
## 5 No Yes Yes Yes Yes
## 6 Yes Yes Yes Yes Yes
dim(cardata)
## [1] 4861 43
str(cardata)
## 'data.frame': 4861 obs. of 43 variables:
## $ Car.Year : int 2019 2019 2019 2019 2019 2019 2019 2018 2018 2018 ...
## $ Car.Make : chr "Acura" "Acura" "Acura" "Acura" ...
## $ Car.Model : chr "RDX " "RDX " "RDX " "RDX " ...
## $ MSRP : int 40600 45500 43600 37400 42600 47500 45600 37500 41000 39700 ...
## $ X2019MSRP : int 40600 45500 43600 37400 42600 47500 45600 38179 41742 40419 ...
## $ EPA.Class : chr "Small Sport Utility Vehicle" "Small Sport Utility Vehicle" "Small Sport Utility Vehicle" "Small Sport Utility Vehicle" ...
## $ Style.Name : chr "FWD w/Technology Pkg" "FWD w/Advance Pkg" "FWD w/A-Spec Pkg" "FWD" ...
## $ Drive.Train : chr "Front Wheel Drive" "Front Wheel Drive" "Front Wheel Drive" "Front Wheel Drive" ...
## $ Passenger.Capacity : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Passenger.Doors : int 4 4 4 4 4 4 4 4 4 4 ...
## $ Body.Style : chr "Sport Utility" "Sport Utility" "Sport Utility" "Sport Utility" ...
## $ Transmittion.Speed : int 10 10 10 10 10 10 10 6 6 6 ...
## $ Base.Curb.Weight : int 3790 3829 3821 3783 4026 4068 4015 3902 3772 3768 ...
## $ Wheelbase : num 108 108 108 108 108 ...
## $ Height.Overall : num 65.7 65.7 65.7 65.7 65.7 65.7 65.7 65 65 65 ...
## $ Fuel.Tank.Capacity : num 17.1 17.1 17.1 17.1 17.1 17.1 17.1 16 16 16 ...
## $ Combined.Estimate.MPG : int 24 24 24 24 23 23 23 22 23 23 ...
## $ City.MPG : int 22 22 22 22 21 21 21 19 20 20 ...
## $ Hwy.MPG : int 28 28 27 28 27 27 26 27 28 28 ...
## $ SAE.Net.Torque : int 280 280 280 280 280 280 280 252 252 252 ...
## $ Fuel.System : chr "Gasoline Injection" "Gasoline Injection" "Gasoline Injection" "Gasoline Injection" ...
## $ Engine.Type : chr "I4" "I4" "I4" "I4" ...
## $ SAE.Net.Horsepower : int 272 272 272 272 272 272 272 279 279 279 ...
## $ Transmittion.Description : chr "Automatic" "Automatic" "Automatic" "Automatic" ...
## $ Brake.Type : chr "4-Wheel Disc" "4-Wheel Disc" "4-Wheel Disc" "4-Wheel Disc" ...
## $ Steering.Type : chr "Power Rack-Pinion" "Power Rack-Pinion" "Power Rack-Pinion" "Power Rack-Pinion" ...
## $ Front.Tire.Size : chr "P235/55HR19" "P235/55HR19" "P255/45VR20" "P235/55HR19" ...
## $ Rear.Tire.Size : chr "P235/55HR19" "P235/55HR19" "P255/45VR20" "P235/55HR19" ...
## $ Front.Tire.Material : chr "Aluminum" "Aluminum" "Aluminum" "Aluminum" ...
## $ Back.Tire.Material : chr "Aluminum" "Aluminum" "Aluminum" "Aluminum" ...
## $ Suspension.Type.Front : chr "Strut" "Strut" "Strut" "Strut" ...
## $ Suspension.Type.Rear : chr "Multi-Link" "Multi-Link" "Multi-Link" "Multi-Link" ...
## $ Brakes.ABS : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Child.Safety.Rear.Door.Locks: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Daytime.Running.Lights : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Traction.Control : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Night.Vision : chr "No" "No" "No" "No" ...
## $ Rollover.Protection.Bars : chr "No" "No" "No" "No" ...
## $ Fog.Lamps : chr "No" "Yes" "Yes" "No" ...
## $ Parking.Aid : chr "Yes" "Yes" "Yes" "No" ...
## $ Tire.Pressure.Monitor : chr "Yes" "Yes" "Yes" "Yes" ...
## $ BackUp.Camera : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Stability.Control : chr "Yes" "Yes" "Yes" "Yes" ...
# Finding the Column names/Variable names
names(cardata)
## [1] "Car.Year" "Car.Make"
## [3] "Car.Model" "MSRP"
## [5] "X2019MSRP" "EPA.Class"
## [7] "Style.Name" "Drive.Train"
## [9] "Passenger.Capacity" "Passenger.Doors"
## [11] "Body.Style" "Transmittion.Speed"
## [13] "Base.Curb.Weight" "Wheelbase"
## [15] "Height.Overall" "Fuel.Tank.Capacity"
## [17] "Combined.Estimate.MPG" "City.MPG"
## [19] "Hwy.MPG" "SAE.Net.Torque"
## [21] "Fuel.System" "Engine.Type"
## [23] "SAE.Net.Horsepower" "Transmittion.Description"
## [25] "Brake.Type" "Steering.Type"
## [27] "Front.Tire.Size" "Rear.Tire.Size"
## [29] "Front.Tire.Material" "Back.Tire.Material"
## [31] "Suspension.Type.Front" "Suspension.Type.Rear"
## [33] "Brakes.ABS" "Child.Safety.Rear.Door.Locks"
## [35] "Daytime.Running.Lights" "Traction.Control"
## [37] "Night.Vision" "Rollover.Protection.Bars"
## [39] "Fog.Lamps" "Parking.Aid"
## [41] "Tire.Pressure.Monitor" "BackUp.Camera"
## [43] "Stability.Control"
# Finding Numerical Variable
numeric_cardata_col <- colnames(cardata[,sapply(cardata,is.numeric)])
numeric_cardata_col
## [1] "Car.Year" "MSRP" "X2019MSRP"
## [4] "Passenger.Capacity" "Passenger.Doors" "Transmittion.Speed"
## [7] "Base.Curb.Weight" "Wheelbase" "Height.Overall"
## [10] "Fuel.Tank.Capacity" "Combined.Estimate.MPG" "City.MPG"
## [13] "Hwy.MPG" "SAE.Net.Torque" "SAE.Net.Horsepower"
cardata_num <- cardata[,c('MSRP','Car.Year','X2019MSRP','Passenger.Capacity','Passenger.Doors','Transmittion.Speed','Base.Curb.Weight','Wheelbase','Height.Overall','Fuel.Tank.Capacity','Combined.Estimate.MPG','City.MPG','Hwy.MPG','SAE.Net.Torque','SAE.Net.Horsepower')]
# Correlation Matrix of Numerical Variable
library(corrplot)
## corrplot 0.92 loaded
c1_6 = round(cor(cardata_num[1:6]),2)
corrplot(c1_6,method ="number")
c7_11 = round(cor(cardata_num[,c(1,7:11)]),2)
corrplot(c7_11,method ="number")
c12_15 = round(cor(cardata_num[,c(1,12:15)]),2)
corrplot(c12_15,method ="number")
ggcorr(cardata_num)
unique(cardata$Car.Year)
## [1] 2019 2018 2016 2015
unique(cardata$Car.Make)
## [1] "Acura" "Alfa" "Aston" "Audi"
## [5] "Bentley" "BMW" "Buick" "Cadillac"
## [9] "Chevrolet" "Chrysler" "Dodge" "Ferrari"
## [13] "FIAT" "Ford" "Genesis" "GMC"
## [17] "Honda" "Hyundai" "INFINITI" "Jaguar"
## [21] "Jeep" "Kia" "Lamborghini" "Land"
## [25] "Lexus" "Lincoln" "Lotus" "Maserati"
## [29] "Mazda" "McLaren" "Mercedes-Benz" "MINI"
## [33] "Mitsubishi" "Nissan" "Porsche" "Ram"
## [37] "Rolls-Royce" "smart" "Subaru" "Toyota"
## [41] "Volkswagen" "Volvo"
#unique(cardata$Car.Model)
#unique(cardata$EPA.Class)
#unique(cardata$Style.Name)
#unique(cardata$Drive.Train)
#unique(cardata$Body.Style)
# Car.Make Vs MSRP & Car.Model Vs MSRP
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
cardata %>%
ggplot() +
aes(y = MSRP, x = Car.Make, group = "") +
geom_point() +
geom_line()+theme(axis.text.x = element_text(angle=90, vjust=0.5, size=8))
cardata %>%
ggplot() +
aes(y = Car.Model, x = Car.Make, group = "") +
geom_point() +
geom_line()+theme(axis.text.x = element_text(angle=90, vjust=0.5, size=8))
unique(cardata$Car.Model)
## [1] "RDX " "MDX "
## [3] "TLX " "ILX "
## [5] "NSX " "RLX "
## [7] "Romeo 4C " "Romeo Giulia "
## [9] "Romeo Stelvio Quadrifoglio " "Romeo Stelvio "
## [11] "Romeo 4C Spider " "Martin DB11 "
## [13] "Martin Vanquish " "A6 "
## [15] "A7 " "Q3 "
## [17] "S3 " "Q5 "
## [19] "S7 " "S4 "
## [21] "Q7 " "A4 "
## [23] "S6 " "A5 "
## [25] "A5 Sportback " "SQ5 "
## [27] "RS 3 " "S5 Coupe "
## [29] "S5 Sportback " "A4 allroad "
## [31] "S5 Cabriolet " "RS 7 "
## [33] "TT " "A8 L "
## [35] "S8 plus " "TT Coupe "
## [37] "RS 5 Coupe " "TTS "
## [39] "RS 5 Sportback " "TT Roadster "
## [41] "Continental GT " "Flying Spur "
## [43] "Mulsanne " "3-Series "
## [45] "X5 " "X3 "
## [47] "8-Series " "M3 "
## [49] "5-Series " "X1 "
## [51] "4-Series " "X4 "
## [53] "Z4 " "M4 "
## [55] "M2 " "X6 "
## [57] "7-Series " "6-Series "
## [59] "2-Series " "M5 "
## [61] "M6 " "Encore "
## [63] "Enclave " "Lacrosse "
## [65] "Regal " "Cascada "
## [67] "Envision " "Regal TourX "
## [69] "Regal Sportback " "XT4 "
## [71] "XT5 " "CT6 "
## [73] "XTS " "CTS "
## [75] "CTS-V " "ATS Coupe "
## [77] "ATS Sedan " "ATS-V Coupe "
## [79] "ATS-V Sedan " "Blazer "
## [81] "Corvette " "Camaro "
## [83] "Equinox " "Traverse "
## [85] "Malibu " "Cruze "
## [87] "Spark " "Impala "
## [89] "Trax " "Sonic "
## [91] "Pacifica " "300"
## [93] "Challenger " "Charger "
## [95] "Journey " "Grand Caravan "
## [97] "488 GTB " "488 Spider "
## [99] "California T " "812 Superfast "
## [101] "GTC4Lusso " "500X "
## [103] "124 Spider " "500"
## [105] "500L " "Mustang "
## [107] "Explorer " "Escape "
## [109] "Edge " "Focus "
## [111] "Ecosport " "Fusion "
## [113] "Transit Connect Wagon " "Taurus "
## [115] "Fiesta " "Flex "
## [117] "G70 " "G80 "
## [119] "G90 " "Acadia "
## [121] "Terrain " "Civic "
## [123] "Civic Coupe " "Pilot "
## [125] "Odyssey " "Passport "
## [127] "HR-V " "Fit "
## [129] "Accord Sedan " "Civic Hatchback "
## [131] "Civic Si Sedan " "Civic Type R "
## [133] "Civic Si Coupe " "Santa Fe "
## [135] "Tucson " "Veloster "
## [137] "Elantra " "Santa Fe Sport "
## [139] "Sonata " "Kona "
## [141] "Accent " "Santa Fe XL "
## [143] "IONIQ Hybrid " "Sonata Hybrid "
## [145] "QX50 " "Q50 "
## [147] "Q70 " "Q60 "
## [149] "QX30 " "F-Pace "
## [151] "F-Type " "XE "
## [153] "XF " "XJ "
## [155] "E-Pace " "Cherokee "
## [157] "Grand Cherokee " "Compass "
## [159] "Renegade " "Sorento "
## [161] "Sportage " "Optima "
## [163] "Niro " "Forte "
## [165] "Soul " "Optima Hybrid "
## [167] "Rio " "K900 "
## [169] "Sedona " "Cadenza "
## [171] "Rio 5-door " "Aventador "
## [173] "Huracan " "Rover Range Rover Sport "
## [175] "Rover Range Rover Evoque " "UX "
## [177] "ES " "RX "
## [179] "RC F " "GS F "
## [181] "NX " "IS "
## [183] "GS " "RC "
## [185] "LS " "LC "
## [187] "Nautilus " "MKC "
## [189] "Continental " "MKX "
## [191] "MKZ " "MKT "
## [193] "Evora " "Ghibli "
## [195] "Levante " "GranTurismo "
## [197] "Quattroporte " "MAZDA3 "
## [199] "MAZDA6 " "MX-5 Miata "
## [201] "CX-3 " "CX-5 "
## [203] "CX-9 " "MX-5 Miata RF "
## [205] "Mazda3 5-Door " "Mazda3 4-Door "
## [207] "570GT " "720S "
## [209] "570S " "A Class "
## [211] "C Class " "CLA Class "
## [213] "GLE Class " "GLC Class "
## [215] "CLS Class " "GLA Class "
## [217] "SL Class " "E Class "
## [219] "SLC Class " "S Class "
## [221] "AMG GT " "Cooper "
## [223] "Cooper Countryman " "Hardtop 2 Door "
## [225] "Clubman " "Hardtop 4 Door "
## [227] "Convertible " "Mirage "
## [229] "Outlander " "Eclipse Cross "
## [231] "Outlander Sport " "Mirage G4 "
## [233] "370Z " "Versa "
## [235] "Sentra " "GT-R "
## [237] "370Z Coupe " "370Z Roadster "
## [239] "Macan " "Panamera "
## [241] "911" "718"
## [243] "718 Cayman " "ProMaster City "
## [245] "Phantom " "Ghost "
## [247] "Wraith " "Dawn "
## [249] "fortwo " "Forester "
## [251] "Crosstrek " "Outback "
## [253] "WRX " "Legacy "
## [255] "Impreza " "BRZ "
## [257] "RAV4 " "Corolla "
## [259] "Avalon " "Highlander "
## [261] "Yaris " "Camry "
## [263] "Sienna " "86"
## [265] "Corolla Hatchback " "Yaris iA "
## [267] "Yaris Sedan " "Corolla iM "
## [269] "Jetta " "Tiguan "
## [271] "Passat " "Golf "
## [273] "Beetle " "Tiguan Limited "
## [275] "Golf Alltrack " "Golf R "
## [277] "Beetle Convertible " "XC40 "
## [279] "XC60 " "V60 "
## [281] "V90 " "S60 "
## [283] "XC90 " "S90 "
## [285] "V60 Cross Country "
# From the graph, it shows that Car.Make == "Lamborghini" has the highest MRSP
highest_MRSP <- cardata[which.max(cardata$MSRP),]
highest_MRSP
## Car.Year Car.Make Car.Model MSRP X2019MSRP EPA.Class
## 2992 2015 Lamborghini Aventador 548800 591936 Two Seaters
## Style.Name Drive.Train Passenger.Capacity
## 2992 2dr Conv 50th Anniversario All Wheel Drive 2
## Passenger.Doors Body.Style Transmittion.Speed Base.Curb.Weight Wheelbase
## 2992 2 Convertible 7 4196 106.3
## Height.Overall Fuel.Tank.Capacity Combined.Estimate.MPG City.MPG Hwy.MPG
## 2992 44.7 23.8 12 10 16
## SAE.Net.Torque Fuel.System Engine.Type SAE.Net.Horsepower
## 2992 508 Gasoline Injection V12 720
## Transmittion.Description Brake.Type Steering.Type Front.Tire.Size
## 2992 Manual 4-Wheel Disc Power Rack-Pinion P255/35YR19
## Rear.Tire.Size Front.Tire.Material Back.Tire.Material
## 2992 P335/30YR20 Aluminum Aluminum
## Suspension.Type.Front Suspension.Type.Rear Brakes.ABS
## 2992 Double Wishbone Pushrod Double Wishbone Pushrod Yes
## Child.Safety.Rear.Door.Locks Daytime.Running.Lights Traction.Control
## 2992 No Yes Yes
## Night.Vision Rollover.Protection.Bars Fog.Lamps Parking.Aid
## 2992 No Yes No No
## Tire.Pressure.Monitor BackUp.Camera Stability.Control
## 2992 Yes No Yes
highest_MRSP_car <- cardata[cardata$Car.Make == "Lamborghini",]
highest_MRSP_car %>%
ggplot() +
aes(y = MSRP, x = Car.Model, group = "") +
geom_point() +
geom_line()+theme(axis.text.x = element_text(angle=90, vjust=0.5, size=8))
Lowest_MRSP <- cardata[which.min(cardata$MSRP),]
Lowest_MRSP
## Car.Year Car.Make Car.Model MSRP X2019MSRP EPA.Class
## 3816 2016 Nissan Versa 11990 12772 Compact Cars
## Style.Name Drive.Train Passenger.Capacity Passenger.Doors
## 3816 4dr Sdn Manual 1.6 S Front Wheel Drive 5 4
## Body.Style Transmittion.Speed Base.Curb.Weight Wheelbase Height.Overall
## 3816 4dr Car 5 2363 102.4 59.6
## Fuel.Tank.Capacity Combined.Estimate.MPG City.MPG Hwy.MPG SAE.Net.Torque
## 3816 10.8 30 27 36 107
## Fuel.System Engine.Type SAE.Net.Horsepower Transmittion.Description
## 3816 Gasoline Injection I4 109 Manual
## Brake.Type Steering.Type Front.Tire.Size Rear.Tire.Size
## 3816 Front Disc/Rear Drum Power Rack-Pinion P185/65HR15 P185/65HR15
## Front.Tire.Material Back.Tire.Material Suspension.Type.Front
## 3816 Steel Steel Strut
## Suspension.Type.Rear Brakes.ABS Child.Safety.Rear.Door.Locks
## 3816 Torsion Beam Yes Yes
## Daytime.Running.Lights Traction.Control Night.Vision
## 3816 No Yes No
## Rollover.Protection.Bars Fog.Lamps Parking.Aid Tire.Pressure.Monitor
## 3816 No No No Yes
## BackUp.Camera Stability.Control
## 3816 No Yes
# From the above R code, it shows that Car.Make == "Nissan" has the lowest MRSP
Lowest_MRSP_car <- cardata[cardata$Car.Make == "Nissan",]
Lowest_MRSP_car %>%
ggplot() +
aes(y = MSRP, x = Car.Model, group = "") +
geom_point() +
geom_line()+theme(axis.text.x = element_text(angle=90, vjust=0.5, size=8))
# Finding the median of MRSP
med <- median(cardata$MSRP)
Median_car <- cardata[cardata$MSRP == 37710,]
Median_car
## Car.Year Car.Make Car.Model MSRP X2019MSRP EPA.Class Style.Name
## 1920 2019 Honda Odyssey 37710 37710 Minivan EX-L Auto
## Drive.Train Passenger.Capacity Passenger.Doors Body.Style
## 1920 Front Wheel Drive 8 4 Mini-van, Passenger
## Transmittion.Speed Base.Curb.Weight Wheelbase Height.Overall
## 1920 9 4471 118.1 69.6
## Fuel.Tank.Capacity Combined.Estimate.MPG City.MPG Hwy.MPG SAE.Net.Torque
## 1920 19.5 22 19 28 262
## Fuel.System Engine.Type SAE.Net.Horsepower Transmittion.Description
## 1920 Gasoline Injection V6 280 Automatic
## Brake.Type Steering.Type Front.Tire.Size Rear.Tire.Size
## 1920 4-Wheel Disc Power Rack-Pinion P235/60HR18 P235/60HR18
## Front.Tire.Material Back.Tire.Material Suspension.Type.Front
## 1920 Aluminum Aluminum Strut
## Suspension.Type.Rear Brakes.ABS Child.Safety.Rear.Door.Locks
## 1920 Trailing Arm Yes Yes
## Daytime.Running.Lights Traction.Control Night.Vision
## 1920 Yes Yes No
## Rollover.Protection.Bars Fog.Lamps Parking.Aid Tire.Pressure.Monitor
## 1920 No Yes No Yes
## BackUp.Camera Stability.Control
## 1920 Yes Yes
Median_car1 <- cardata[cardata$Car.Make == "Honda",]
Median_car1 %>%
ggplot() +
aes(y = MSRP, x = Car.Model, group = "") +
geom_point() +
geom_line()+theme(axis.text.x = element_text(angle=90, vjust=0.5, size=8))
plot(cardata_num)
par(mfrow=c(2,3))
plot(cardata_num$Car.Year, cardata_num$MSRP, main="Car.Year Vs MSRP", xlab="Car.Year", ylab="MSRP", col="red", cex=2)
plot(cardata_num$X2019MSRP, cardata_num$MSRP, main="X2019MSRP Vs MSRP", xlab="X2019MSRP", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Passenger.Capacity, cardata_num$MSRP, main="Passenger.Capacity Vs MSRP", xlab="Passenger.Capacity", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Passenger.Doors, cardata_num$MSRP, main="Passenger.Doors Vs MSRP", xlab="Passenger.Doors", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Transmittion.Speed, cardata_num$MSRP, main="Transmittion.Speed Vs MSRP", xlab="Transmittion.Speed", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Base.Curb.Weight, cardata_num$MSRP, main="Base.Curb.Weight Vs MSRP", xlab="Base.Curb.Weight", ylab="MSRP", col="red", cex=2)
par(mfrow=c(3,3))
plot(cardata_num$Wheelbase, cardata_num$MSRP, main="Wheelbase Vs MSRP", xlab="Wheelbase", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Height.Overall, cardata_num$MSRP, main="Height.Overall Vs MSRP", xlab="Height.Overall", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Fuel.Tank.Capacity, cardata_num$MSRP, main="Fuel.Tank.Capacity Vs MSRP", xlab="Fuel.Tank.Capacity", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Combined.Estimate.MPG, cardata_num$MSRP, main="Combined.Estimate.MPG Vs MSRP", xlab="Combined.Estimate.MPG", ylab="MSRP", col="red", cex=2)
plot(cardata_num$City.MPG, cardata_num$MSRP, main="City.MPG Vs MSRP", xlab="City.MPG", ylab="MSRP", col="red", cex=2)
plot(cardata_num$Hwy.MPG, cardata_num$MSRP, main="Hwy.MPG Vs MSRP", xlab="Hwy.MPG", ylab="MSRP", col="red", cex=2)
plot(cardata_num$SAE.Net.Torque, cardata_num$MSRP, main="SAE.Net.Torque Vs MSRP", xlab="SAE.Net.Torque", ylab="MSRP", col="red", cex=2)
plot(cardata_num$SAE.Net.Horsepower, cardata_num$MSRP, main="SAE.Net.Horsepower Vs MSRP", xlab="SAE.Net.Horsepower", ylab="MSRP", col="red", cex=2)
boxplot(cardata$MSRP~as.factor(cardata$EPA.Class),main="EPA.Class Vs MSRP",xlab="EPA.Class",ylab="MSRP",col="yellow",border="red")
boxplot(cardata$MSRP~as.factor(cardata$Drive.Train),main="Drive.Train Vs MSRP",xlab="Drive.Train",ylab="MSRP",col="yellow",border="red")
boxplot(cardata$MSRP~as.factor(cardata$Body.Style),main="Body.Style Vs MSRP",xlab="Body.Style",ylab="MSRP",col="yellow",border="red")
boxplot(cardata$MSRP~as.factor(cardata$Fuel.System),main="Fuel.System Vs MSRP",xlab="Fuel.System",ylab="MSRP",col="yellow",border="red")
boxplot(cardata$MSRP~as.factor(cardata$Engine.Type),main="Engine.Type Vs MSRP",xlab="Engine.Type",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Transmittion.Description),main="Transmittion.Description Vs MSRP",xlab="Transmittion.Description",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Brake.Type),main="Brake.Type Vs MSRP",xlab="Brake.Type",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Steering.Type),main="Steering.Type Vs MSRP",xlab="Steering.Type",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Front.Tire.Material),main="Front.Tire.Material Vs MSRP",xlab="Front.Tire.Material",ylab="MSRP",col="yellow",border="blue")
boxplot(cardata$MSRP~as.factor(cardata$Back.Tire.Material),main="Back.Tire.Material Vs MSRP",xlab="Back.Tire.Material",ylab="MSRP",col="yellow",border="black")
boxplot(cardata$MSRP~as.factor(cardata$Suspension.Type.Front),main="Suspension.Type.Front Vs MSRP",xlab="Suspension.Type.Front",ylab="MSRP",col="yellow",border="blue")
boxplot(cardata$MSRP~as.factor(cardata$Suspension.Type.Rear),main="Suspension.Type.Rear Vs MSRP",xlab="Suspension.Type.Rear",ylab="MSRP",col="yellow",border="black")
boxplot(cardata$MSRP~as.factor(cardata$Brakes.ABS),main="Brakes.ABS Vs MSRP",xlab="Brakes.ABS",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~cardata$Child.Safety.Rear.Door.Locks,main="Child.Safety.Rear.Door.Locks Vs MSRP",xlab="Child.Safety.Rear.Door.Locks",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Daytime.Running.Lights),main="Daytime.Running.Lights Vs MSRP",xlab="Daytime.Running.Lights",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Traction.Control),main="Traction.Control Vs MSRP",xlab="Traction.Control",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Night.Vision),main="Night.Vision Vs MSRP",xlab="Night.Vision",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Rollover.Protection.Bars),main="Rollover.Protection.Bars Vs MSRP",xlab="Rollover.Protection.Bars",ylab="MSRP",col="orange",border="brown")
boxplot(cardata$MSRP~as.factor(cardata$Fog.Lamps),main="Fog.Lamps Vs MSRP",xlab="Fog.Lamps",ylab="MSRP",col="gold",border="black")
boxplot(cardata$MSRP~as.factor(cardata$Parking.Aid),main="Parking.Aid Vs MSRP",xlab="Parking.Aid",ylab="MSRP",col="gold",border="black")
boxplot(cardata$MSRP~as.factor(cardata$Tire.Pressure.Monitor),main="Tire.Pressure.Monitor Vs MSRP",xlab="Tire.Pressure.Monitor",ylab="MSRP",col="gold",border="black")
boxplot(cardata$MSRP~as.factor(cardata$BackUp.Camera),main="BackUp.Camera Vs MSRP",xlab="BackUp.Camera",ylab="MSRP",col="gold",border="black")
boxplot(cardata$MSRP~as.factor(cardata$Stability.Control),main="Stability.Control Vs MSRP",xlab="Stability.Control",ylab="MSRP",col="gold",border="black")
